论文标题
SSN：用于图像合成的软影网络
SSN: Soft Shadow Network for Image Compositing
论文作者
论文摘要
我们介绍了一个交互式软影网络（SSN），以生成可控的软影片，用于图像合成。 SSN将2D对象掩码作为输入，因此对诸如绘画和矢量艺术之类的图像类型不可知。环境光图用于控制阴影的特征，例如角度和柔软度。 SSN采用环境遮挡预测模块来预测中间环境遮挡图，用户可以进一步完善该模块，以提供几何提示来调节阴影的生成。为了训练我们的模型，我们设计了一条有效的管道，以使用3D对象模型生成各种柔软的训练数据。此外，我们提出了一个反向阴影图表示，以改善模型训练。我们证明我们的模型会实时产生逼真的柔和阴影。我们的用户研究表明，生成的阴影通常与基于物理学的渲染器计算出的阴影无法区分，用户可以通过交互式应用程序轻松地使用SSN来在几分钟内生成特定的阴影效果。
We introduce an interactive Soft Shadow Network (SSN) to generates controllable soft shadows for image compositing. SSN takes a 2D object mask as input and thus is agnostic to image types such as painting and vector art. An environment light map is used to control the shadow's characteristics, such as angle and softness. SSN employs an Ambient Occlusion Prediction module to predict an intermediate ambient occlusion map, which can be further refined by the user to provides geometric cues to modulate the shadow generation. To train our model, we design an efficient pipeline to produce diverse soft shadow training data using 3D object models. In addition, we propose an inverse shadow map representation to improve model training. We demonstrate that our model produces realistic soft shadows in real-time. Our user studies show that the generated shadows are often indistinguishable from shadows calculated by a physics-based renderer and users can easily use SSN through an interactive application to generate specific shadow effects in minutes.
